VictorMylle/Kohya-ss-sd-scripts
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add dtype to u-net loading

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Isotr0py
2023-07-27 23:58:25 +08:00
parent 50544b7805
commit 96a52d9810
2 changed files with 7 additions and 4 deletions
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6
library/sdxl_model_util.py
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@@ -135,7 +135,7 @@ def convert_sdxl_text_encoder_2_checkpoint(checkpoint, max_length):
return new_sd, logit_scale return new_sd, logit_scale
def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location): def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype):
# model_version is reserved for future use # model_version is reserved for future use
# Load the state dict # Load the state dict
@@ -167,7 +167,9 @@ def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location):
print("loading U-Net from checkpoint") print("loading U-Net from checkpoint")
for k in list(state_dict.keys()): for k in list(state_dict.keys()):
if k.startswith("model.diffusion_model."): if k.startswith("model.diffusion_model."):
set_module_tensor_to_device(unet, k.replace("model.diffusion_model.", ""), map_location, value=state_dict.pop(k)) set_module_tensor_to_device(
unet, k.replace("model.diffusion_model.", ""), map_location, value=state_dict.pop(k), dtype=dtype
)
# TODO: catch missing_keys and unexpected_keys with _IncompatibleKeys # TODO: catch missing_keys and unexpected_keys with _IncompatibleKeys
# print("U-Net: ", info) # print("U-Net: ", info)

5
library/sdxl_train_util.py
View File

@@ -54,6 +54,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
def _load_target_model(args: argparse.Namespace, model_version: str, weight_dtype, device="cpu"): def _load_target_model(args: argparse.Namespace, model_version: str, weight_dtype, device="cpu"):
# TODO: integrate full fp16/bf16 to model loading
name_or_path = args.pretrained_model_name_or_path name_or_path = args.pretrained_model_name_or_path
name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path
load_stable_diffusion_format = os.path.isfile(name_or_path) # determine SD or Diffusers load_stable_diffusion_format = os.path.isfile(name_or_path) # determine SD or Diffusers
@@ -67,7 +68,7 @@ def _load_target_model(args: argparse.Namespace, model_version: str, weight_dtyp
unet, unet,
logit_scale, logit_scale,
ckpt_info, ckpt_info,
) = sdxl_model_util.load_models_from_sdxl_checkpoint(model_version, name_or_path, device) ) = sdxl_model_util.load_models_from_sdxl_checkpoint(model_version, name_or_path, device, weight_dtype)
else: else:
# Diffusers model is loaded to CPU # Diffusers model is loaded to CPU
variant = "fp16" if weight_dtype == torch.float16 else None variant = "fp16" if weight_dtype == torch.float16 else None
@@ -98,7 +99,7 @@ def _load_target_model(args: argparse.Namespace, model_version: str, weight_dtyp
with init_empty_weights(): with init_empty_weights():
unet = sdxl_original_unet.SdxlUNet2DConditionModel() unet = sdxl_original_unet.SdxlUNet2DConditionModel()
for k in list(state_dict.keys()): for k in list(state_dict.keys()):
set_module_tensor_to_device(unet, k, device, value=state_dict.pop(k)) set_module_tensor_to_device(unet, k, device, value=state_dict.pop(k), dtype=weight_dtype)
print("U-Net converted to original U-Net") print("U-Net converted to original U-Net")
logit_scale = None logit_scale = None